A volatile organic compound monitoring system for hydrocarbon removal

Abstract

This invention relates to the field of environmental science and technology, specifically to a volatile organic compound (VOC) monitoring system using a hydrocarbon removal instrument. The system includes a sample acquisition module, a concentration distribution map generation module, a diffusion trend analysis module, a meteorological impact analysis module, a model building module, and a management database. This system determines the diffusion direction and intensity of VOCs by drawing VOC concentration contour maps, analyzes the influence coefficient of meteorological conditions on VOC diffusion by collecting concurrent meteorological data, and outputs VOC diffusion predictions through model building. This helps improve the intelligence and precision of VOC monitoring, enabling real-time and accurate understanding of VOC dynamic changes and timely detection of potential pollution problems.

Description

Technical Field

[0001] This invention relates to the field of environmental science and technology, and more specifically, to a hydrocarbon removal instrument volatile organic compound monitoring system. Background Technology

[0002] With the acceleration of industrialization and urbanization, the emission of volatile organic compounds (VOCs) is increasing, posing a serious threat to the environment and human health. VOCs are not only important precursors to air pollution problems such as photochemical smog and acid rain, but may also cause health problems such as respiratory diseases and nervous system damage. Therefore, accurate and efficient monitoring of VOC concentration and composition is of great significance for air pollution prevention and control and environmental protection. As an instrument specifically designed to remove hydrocarbon compounds from sample gas, the hydrocarbon removal instrument plays a key role in VOC monitoring. It can effectively eliminate the interference of hydrocarbons on monitoring results and improve the accuracy and reliability of monitoring data.

[0003] However, current methods for monitoring volatile organic compounds (VOCs) have many limitations. For example, Chinese patent application number 202110433610.0 discloses an online monitoring method and system for VOCs. This method obtains the monitoring datasets corresponding to each monitoring thread, filters the first monitoring time set according to the length of each monitoring time set, finds the subset with equal time values ​​by matching time values, determines the monitoring datasets of the corresponding VOC monitoring threads based on the determined subsets and the second monitoring time set, calculates the matching coefficient between the monitoring datasets of any two VOC monitoring threads, and determines the two monitoring threads with matching coefficients greater than a preset threshold as the global monitoring results for VOCs.

[0004] However, this scheme has some shortcomings: First, the distribution of volatile organic compounds has spatial characteristics, and the concentration and composition may vary greatly in different geographical locations. When processing monitoring data, this scheme does not consider spatial dimension information, and cannot analyze the distribution characteristics and diffusion trends of volatile organic compounds in different regions, which is not conducive to understanding their diffusion and propagation laws from a macroscopic perspective.

[0005] Second, in the process of monitoring volatile organic compounds, it is necessary to take into account the influence of external factors. For example, wind direction and wind speed in meteorological conditions have a direct impact on the diffusion of volatile organic compounds. This scheme does not take meteorological conditions into account, which may prevent the analysis of the impact of real-time meteorological changes on monitoring results. It is difficult to accurately reflect the real situation of volatile organic compounds at different times and locations, thus reducing the timeliness and practicality of monitoring data. Summary of the Invention

[0006] To overcome the shortcomings of the prior art, embodiments of the present invention provide a volatile organic compound monitoring system for hydrocarbon removal, which can effectively solve the problems involved in the prior art.

[0007] The objective of this invention can be achieved through the following technical solution: This invention provides a hydrocarbon removal instrument volatile organic compound monitoring system, including: a sample collection module for collecting air samples from the environment and performing preprocessing.

[0008] The concentration distribution map generation module uses a spatial interpolation algorithm to draw isopleth maps of volatile organic compound concentrations based on the estimated concentration values ​​of interpolation points in each sub-region.

[0009] The diffusion trend analysis module is used to determine the diffusion direction and intensity of volatile organic compounds by comparing the concentration changes at sampling points at adjacent time points.

[0010] The meteorological impact analysis module is used to collect meteorological data for the same period and analyze the impact coefficient of meteorological conditions on the diffusion of volatile organic compounds.

[0011] The model building module is used to build models based on the diffusion direction and intensity of volatile organic compounds (VOCs) in each sub-region at each time period, as well as the influence coefficient of meteorological conditions on VOC diffusion at each time period, and output VOC diffusion predictions.

[0012] The management database stores the collected parameters and volatile organic compound concentration data tables for each ambient air quality range.

[0013] Preferably, the specific operation method of the sample collection module is as follows: the environmental area to be collected is divided into grids to obtain sub-regions, and each detection point is set in each sub-region according to the principle of equal spacing. The single collection time of the hydrocarbon removal instrument is set, and each time point is set according to the preset equal time interval principle. The collection flow rate is set. According to the single collection time of the hydrocarbon removal instrument, the ambient air at each detection point in each sub-region is collected by the hydrocarbon removal instrument at each time point to obtain air samples at each detection point in each sub-region at each time point. The collected air samples at each detection point in each sub-region at each time point are put into the hydrocarbon removal instrument and the hydrocarbon substances are removed by adsorption technology.

[0014] Preferably, the sample collection module performs air quality testing on the environmental area to be sampled before collecting air samples, and provides real-time feedback on the collection parameters corresponding to each environmental air quality range stored in the management database, dynamically adjusting the quantity and collection flow rate at each time point.

[0015] Preferably, the specific analysis method for the concentration estimation values ​​of the interpolation points in each sub-region is as follows: obtain the corresponding volatile organic compound concentration values ​​from the air samples at each time point of each detection point after processing, organize them according to the location coordinates of each detection point and the corresponding time point, form a volatile organic compound concentration data table and store it in the management database.

[0016] Using each sub-region as an interpolation unit, the center point of each interpolation unit is selected as the interpolation point. The distance between each sub-region interpolation point and each detection point in its interpolation unit is obtained. The interpolation points in each sub-region and each detection point in its interpolation unit are numbered from closest to furthest, as 1, 2, ..., m, ..., q. The concentration estimate of each sub-region interpolation point is then calculated. Where j represents the number of the j-th sub-region, j = 1, 2, ..., k, D jm C represents the distance between the j-th sub-region interpolation point and the m-th detection point in its interpolation unit. jm This represents the concentration of volatile organic compounds corresponding to the m-th detection point in the interpolation unit where the j-th sub-region interpolation point is located.

[0017] Preferably, the specific operation method of the concentration distribution map generation module is as follows: obtain the minimum and maximum values ​​from the concentration estimates of the interpolation points of each sub-region, obtain the concentration range of volatile organic compounds in the environmental area to be collected in sequence, set the interval of the contour lines, determine the number of contour lines to be drawn in combination with the concentration range of volatile organic compounds in the environmental area to be collected, set a color gradient for the concentration range of volatile organic compounds in the environmental area to be collected, map the concentration estimates of the interpolation points of each sub-region onto the set color gradient, and draw the volatile organic compound concentration contour map of each sub-region at each time point according to the color corresponding to the concentration estimates of the interpolation points of each sub-region.

[0018] Preferably, the specific operation steps of the diffusion trend analysis module include: S1. Selecting a number of sampling points from the volatile organic compound concentration contour maps of each sub-region at each time point according to a set interval, and taking adjacent time points as a time period, comparing the volatile organic compound concentration changes of each sampling point in each sub-region at each time period based on the volatile organic compound concentration contour maps of each sub-region at each time point.

[0019] S2. Based on the changes in volatile organic compound (VOC) concentration at each sampling point in each sub-region at each time period, determine the diffusion direction of VOC in each sub-region at each time period.

[0020] S3. Calculate the diffusion intensity of volatile organic compounds in each sub-region and each sub-region at each time period based on the changes in volatile organic compound concentration at each sampling point in each sub-region.

[0021] Preferably, the specific operation method of step S2 is as follows: S21. Sort the volatile organic compound concentration contour maps of each sub-region at each time point according to the time point order, read the position coordinates of each sampling point and the volatile organic compound concentration value of the corresponding time point in the volatile organic compound concentration contour maps of each sub-region at each time point, and record them as the volatile organic compound concentration values ​​of each sampling point in each sub-region at each time point. At the same time, taking adjacent time points as a time period, calculate the difference between the volatile organic compound concentration values ​​of each sampling point in each sub-region at adjacent time points to obtain the change in volatile organic compound concentration of each sampling point in each sub-region in each time period.

[0022] S22. If the change in volatile organic compound (VOC) concentration at a sampling point in a sub-region during a certain period is equal to 0, it means that the VOC concentration at that sampling point in that sub-region during that period remains unchanged. If the change in VOC concentration at a sampling point in a sub-region during a certain period is greater than 0, it means that the VOC concentration at that sampling point in that sub-region during that period increases. Conversely, if the change in VOC concentration at that sampling point in that sub-region during that period decreases, it means that the VOC concentration at that sampling point in that sub-region decreases.

[0023] S23. Select the sampling points where the concentration of volatile organic compounds (VOCs) increases in each sub-region during each time period and record them as reference sampling points. Set the corresponding angle thresholds for each direction to divide the direction of each reference sampling point relative to the center point of each sub-region. If the concentration of VOCs at each reference sampling point in a certain direction in a certain sub-region increases, while the concentration of VOCs at each reference sampling point in other directions remains unchanged or decreases, then determine that direction as the diffusion direction of VOCs concentration in that sub-region during that time period. Thus, the diffusion direction of VOCs in each sub-region during each time period is obtained.

[0024] Preferably, the specific operation method of step S3 is as follows: obtain the duration of each time period, select each reference sampling point in the diffusion direction of volatile organic compounds in each sub-region of each time period, and record them as each diffusion sampling point. Take adjacent diffusion sampling points as a group, and obtain the straight-line distance d between each group of adjacent diffusion sampling points in each sub-region. jf The concentration change ΔC of volatile organic compounds at adjacent diffusion sampling points in each time period, sub-region, and group. ijf Let i represent the number of the i-th time period, i = 1, 2, ..., n, and f represent the number of the f-th group of adjacent diffusion sampling points, f = 1, 2, ..., F. The formula is used to... The diffusion intensity I of volatile organic compounds in each sub-region at each time period was obtained. ij , where s j t represents the number of adjacent diffusion sampling points in the j-th sub-region. i This represents the duration of the i-th time period.

[0025] Preferably, the specific analysis method of the meteorological impact analysis module is as follows: obtaining the position coordinates (x, y) of the first diffusion sampling point and the last diffusion sampling point in the diffusion direction of volatile organic compounds in each sub-region at each time period. ij1 ,y ij1 (x) ij2 ,y ij2 ), x ij1 y ij1 The x and y coordinates represent the first diffusion sampling point in the j-th sub-region during the i-th time period, respectively. ij2 y ij2 Let x and y represent the x-coordinates and y-coordinates of the final diffusion sampling point in the j-th sub-region during the i-th time period. The diffusion direction of volatile organic compound (VOC) concentration is converted into a vector and normalized to obtain the unit diffusion direction vector of VOCs in each sub-region during each time period.

[0026]

[0027] Wind direction and speed were acquired concurrently with the monitoring of volatile organic compounds (VOCs). These wind directions and speeds were then mapped one-to-one with each sampling point at each time point. The average values ​​were calculated to obtain the wind direction and speed for each sub-region at each time period. The influence coefficient of meteorological conditions on VOC diffusion at each time period was then calculated. Among them I ij 、W ij These represent the diffusion intensity and wind speed of the volatile organic compound concentration in the j-th sub-region during the i-th time period, respectively. These are the average values ​​of the diffusion intensity and wind speed of the volatile organic compound concentration during the i-th time period, respectively. φ1 represents the unit direction vector of the wind direction in the j-th sub-region during the i-th time period, and φ2 represents the weighting factors of wind direction and wind speed, respectively.

[0028] Preferably, the specific operation method of the model construction module is as follows: based on the diffusion direction, diffusion intensity, and influence coefficient of meteorological conditions on the diffusion of volatile organic compounds in each sub-region at each time period, a model is trained to generate a volatile organic compound diffusion prediction model. The location coordinates of the detection points for demand analysis, the demand analysis time period and its corresponding meteorological conditions are input into the trained model to output the diffusion prediction of volatile organic compounds.

[0029] Compared with the prior art, the present invention has the following beneficial effects: First, by drawing isopleth maps of volatile organic compound concentration, the present invention determines the diffusion direction and diffusion intensity of volatile organic compounds, and can clearly display the concentration distribution of volatile organic compounds in a specific area in a visual manner, and accurately determine the diffusion direction of volatile organic compounds.

[0030] Second, this invention collects meteorological data from the same period and analyzes the influence coefficient of meteorological conditions on the diffusion of volatile organic compounds. By clarifying the relationship between meteorological conditions and the diffusion of volatile organic compounds, it can provide a more accurate basis for predicting the diffusion of volatile organic compounds.

[0031] Third, this invention constructs a model to output a diffusion prediction of volatile organic compounds, which can predict the future diffusion of volatile organic compounds and take measures in advance based on the prediction results to reduce the risks of volatile organic compounds to the environment and human health. Attached Figure Description

[0032] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 This is a system module connection diagram of the present invention.

[0034] Figure 2 This is a flowchart illustrating the diffusion trend analysis module.

[0035] Figure 3 for Figure 2 A flowchart illustrating step S2. Detailed Implementation

[0036] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] Please see Figure 1 As shown, a hydrocarbon removal instrument volatile organic compound monitoring system includes a sample acquisition module, a concentration distribution map generation module, a diffusion trend analysis module, a meteorological impact analysis module, a model building module, and a management database.

[0038] The management database is connected to the concentration distribution map generation module, the diffusion trend analysis module, the meteorological impact analysis module, and the model building module. The model building module is connected to the diffusion trend analysis module and the meteorological impact analysis module. The diffusion trend analysis module is connected to the concentration distribution map generation module and the meteorological impact analysis module. The sample collection module is connected to the concentration distribution map generation module.

[0039] The sample collection module is used to collect air samples from the environment and perform preprocessing.

[0040] The specific operation method of the sample collection module is as follows: the environmental area to be collected is divided into grids to obtain sub-regions, and each detection point is set in each sub-region according to the principle of equal spacing. The single collection time of the hydrocarbon removal instrument is set, and each time point is set according to the preset equal time interval principle. The collection flow rate is set. According to the single collection time of the hydrocarbon removal instrument, the ambient air at each detection point in each sub-region is collected by the hydrocarbon removal instrument at each time point to obtain air samples at each detection point in each sub-region at each time point. The collected air samples at each detection point in each sub-region at each time point are put into the hydrocarbon removal instrument, and the hydrocarbon substances are removed by adsorption technology. The gridding and equal spacing of the points can comprehensively and uniformly cover the environmental area, ensuring that the collected samples are more representative and accurately reflect the environmental conditions of the entire area. The removal of hydrocarbon substances helps to accurately analyze other components and eliminate hydrocarbon interference.

[0041] Before collecting air samples, the sample collection module detects the air quality of the environmental area to be sampled, and provides real-time feedback based on the collection parameters corresponding to each environmental air quality range stored in the management database, dynamically adjusting the quantity and collection flow rate at each time point.

[0042] The concentration distribution map generation module uses a spatial interpolation algorithm to draw isopleth maps of volatile organic compound concentrations based on the estimated concentration values ​​of interpolation points in each sub-region.

[0043] The specific analysis method for the concentration estimates of the interpolation points in each sub-region is as follows: obtain the corresponding volatile organic compound (VOC) concentration values ​​from the air samples at each time point after processing, organize them according to the location coordinates of each detection point and the corresponding time point, and store the VOC concentration data table in the management database; obtaining the concentration values ​​and organizing them into a table facilitates the management, querying and subsequent analysis of VOC concentration data, and helps to establish a data resource library.

[0044] Please refer to Table 1 for details, which lists some representative data.

[0045] Table 1. Partially collected volatile organic compound concentration data

[0046]

[0047] Using each sub-region as an interpolation unit, the center point of each interpolation unit is selected as the interpolation point. The distance between each sub-region interpolation point and each detection point in its interpolation unit is obtained. The interpolation points in each sub-region and each detection point in its interpolation unit are numbered from closest to furthest, as 1, 2, ..., m, ..., q. The concentration estimate of each sub-region interpolation point is then calculated. Where j represents the number of the j-th sub-region, j = 1, 2, ..., k, D jm C represents the distance between the j-th sub-region interpolation point and the m-th detection point in its interpolation unit. jm This represents the volatile organic compound (VOC) concentration corresponding to the m-th detection point within the interpolation unit where the j-th sub-region interpolation point is located. By interpolating and calculating the estimated concentration of the sub-region interpolation point, the situation of the entire sub-region can be estimated using limited detection point data, filling data gaps and providing a more comprehensive understanding of the VOC concentration distribution.

[0048] The concentration estimation formula for each sub-region divides the entire study area into multiple sub-regions. An interpolation unit is a region defined around the interpolation point. Sampling points within this region provide concentration information. The core idea of ​​the formula is to estimate the concentration at the interpolation point based on the principle of distance-weighted averaging. The numerator... The closer the detection point is to the interpolation point, the greater its concentration value carries in the calculation; conversely, the farther the detection point is, the smaller its concentration value has on the concentration estimation of the interpolation point. Then, all detection points... The values ​​are summed to obtain a comprehensive value that reflects the concentration at each detection point and its influence on the interpolation point. The denominator is... The reciprocal of the distance from each detection point to the interpolation point The molecules are accumulated and normalized to ensure that the final concentration estimate is within a reasonable range.

[0049] It should be noted that, in one specific embodiment, there is a sub-region with three detection points within its interpolation unit. Detection point 1 has a concentration of 10 and is 2 units away from the interpolation point; detection point 2 has a concentration of 15 and is 3 units away from the interpolation point; and detection point 3 has a concentration of 8 and is 4 units away from the interpolation point. Then the molecular... denominator The concentration estimate at the interpolation point in this sub-region is:

[0050]

[0051] The specific operation method of the concentration distribution map generation module is as follows: The minimum and maximum values ​​are obtained from the concentration estimates of the interpolation points in each sub-region, and the concentration range of volatile organic compounds (VOCs) in the environmental area to be collected is obtained sequentially. The interval of the contour lines is set, and the number of contour lines to be drawn is determined based on the concentration range of VOCs in the environmental area to be collected. Simultaneously, a color gradient is set for the concentration range of VOCs in the environmental area to be collected. The concentration estimates of the interpolation points in each sub-region are mapped onto the set color gradient, and the VOC concentration contour maps for each sub-region at each time point are drawn according to the colors corresponding to the concentration estimates of the interpolation points in each sub-region. This can intuitively present the distribution differences and changing trends of VOC concentrations in each sub-region at different time points.

[0052] The diffusion trend analysis module is used to determine the diffusion direction and intensity of volatile organic compounds by comparing the concentration changes at sampling points at adjacent time points.

[0053] Please see Figure 2 As shown, the specific operation steps of the diffusion trend analysis module include: S1. Selecting several sampling points from the volatile organic compound concentration contour maps of each sub-region at each time point according to a set interval, and taking adjacent time points as a time period, comparing the volatile organic compound concentration changes of each sampling point in each sub-region at each time point based on the volatile organic compound concentration contour maps of each sub-region at each time point; selecting sampling points and comparing concentration changes helps to quantify the dynamic changes of volatile organic compound concentrations in each sub-region at different time periods.

[0054] S2. Based on the changes in volatile organic compound (VOC) concentration at each sampling point in each sub-region at each time period, determine the diffusion direction of VOC in each sub-region at each time period; determining the diffusion direction based on the concentration change can intuitively present the migration trend of VOC in each sub-region at different times.

[0055] S3. Calculate the diffusion intensity of volatile organic compounds in each sub-region and each sub-region at each sampling point based on the changes in volatile organic compound concentrations over each time period. By calculating the diffusion intensity based on the concentration changes, the diffusion capacity of volatile organic compounds in each sub-region at each time period can be quantitatively analyzed.

[0056] Please see Figure 3As shown, the specific operation method of step S2 is as follows: S21. Sort the volatile organic compound concentration contour maps of each sub-region at each time point according to the time point order, read the position coordinates of each sampling point and the corresponding volatile organic compound concentration value at each time point in the volatile organic compound concentration contour maps of each sub-region at each time point, and record them as the volatile organic compound concentration values ​​of each sampling point in each sub-region at each time point. At the same time, taking adjacent time points as a time period, the difference between the volatile organic compound concentration values ​​of each sampling point in each sub-region at adjacent time points is calculated to obtain the volatile organic compound concentration change of each sampling point in each sub-region in each time period. By operating the contour maps at each time point to obtain the concentration change, the change of volatile organic compound concentration of each sampling point in each sub-region at different time periods can be accurately quantified.

[0057] S22. If the change in volatile organic compound (VOC) concentration at a sampling point in a sub-region during a certain time period is 0, it indicates that the VOC concentration at that sampling point in that sub-region during that time period remains unchanged. If the change in VOC concentration at a sampling point in a sub-region during a certain time period is greater than 0, it indicates that the VOC concentration at that sampling point in that sub-region during that time period increases. Conversely, if the change in concentration is greater than 0, it indicates that the VOC concentration at that sampling point in that sub-region during that time period decreases. Based on the change in concentration, the increase or decrease in concentration can be determined, and the trend of VOC concentration changes at each sampling point in each sub-region during each time period can be clarified.

[0058] S23. Select the sampling points where the concentration of volatile organic compounds (VOCs) increases in each sub-region during each time period and record them as reference sampling points. Set the corresponding angle thresholds for each direction to divide the direction of each reference sampling point relative to the center point of each sub-region. If the concentration of VOCs at each reference sampling point in a certain direction in a certain sub-region increases, while the concentration of VOCs at each reference sampling point in other directions remains unchanged or decreases, then determine that direction as the diffusion direction of VOCs concentration in that sub-region during that time period. This yields the diffusion direction of VOCs in each sub-region during each time period. Selecting reference sampling points and determining the diffusion direction helps to determine the specific diffusion direction of VOCs in each sub-region at different time periods.

[0059] The specific operation method of step S3 is as follows: obtain the duration of each time period, select each reference sampling point in the diffusion direction of volatile organic compounds in each sub-region of each time period, and record them as each diffusion sampling point. Take adjacent diffusion sampling points as a group, and obtain the straight-line distance d between each group of adjacent diffusion sampling points in each sub-region. jf The concentration change ΔC of volatile organic compounds at adjacent diffusion sampling points in each time period, sub-region, and group. ijf Let i represent the number of the i-th time period, i = 1, 2, ..., n, and f represent the number of the f-th group of adjacent diffusion sampling points, f = 1, 2, ..., F. The formula is used to... The diffusion intensity I of volatile organic compounds in each sub-region at each time period was obtained. ij, where s j t represents the number of adjacent diffusion sampling points in the j-th sub-region. i It represents the duration of the i-th time period; it can quantitatively measure the diffusion capacity of volatile organic compounds in each sub-region during each time period, which helps to analyze the diffusion characteristics of volatile organic compounds in depth.

[0060] It should be noted that ΔC ijf This represents the concentration change among adjacent sampling points in the j-th sub-region within the i-th time period. It directly reflects the degree of change in volatile organic compound concentration among these sampling points due to diffusion. The greater the concentration change, the more significant the diffusion effect. Dividing the concentration change by the duration of the time period yields the concentration change per unit time. This represents the rate of change of volatile organic compound concentration per unit time between adjacent sampling points in the j-th sub-region of the i-th time period, group f. Considering that diffusion occurs in space, distance factors also need to be taken into account, with the straight-line distance d between adjacent diffusion sampling points being... jf This represents the spatial scale of diffusion. Dividing the rate of concentration change per unit time by the straight-line distance between adjacent diffusion sampling points yields the change in volatile organic compound concentration per unit time and unit distance. It more accurately reflects the diffusion situation at a specific spatial and temporal scale. To comprehensively reflect the diffusion intensity of the sub-region in the i-th time period, the mean value is finally calculated.

[0061]

[0062] The meteorological impact analysis module is used to collect meteorological data for the same period and analyze the impact coefficient of meteorological conditions on the diffusion of volatile organic compounds.

[0063] The specific analysis method of the meteorological impact analysis module is as follows: Obtain the position coordinates (x, y) of the first and last diffusion sampling points along the diffusion direction of volatile organic compounds in each sub-region at each time period. ij1 ,y ij1 (x) ij2 ,y ij2 ), x ij1 、y ij1 The x and y coordinates represent the first diffusion sampling point in the j-th sub-region during the i-th time period, respectively. ij2 、y ij2 Let x and y represent the x-coordinates and y-coordinates of the final diffusion sampling point in the j-th sub-region during the i-th time period. The diffusion direction of volatile organic compound (VOC) concentration is converted into a vector and normalized to obtain the unit diffusion direction vector of VOCs in each sub-region during each time period. By determining the coordinates of the first and last sampling points, the diffusion unit direction vector can be obtained, which can accurately quantify the diffusion direction of volatile organic compounds in each sub-region at each time period, providing accurate directional data for subsequent analysis.

[0064] It should be noted that the expression for the diffusion unit direction vector of volatile organic compounds in each sub-region at each time period consists of two main parts: in a two-dimensional Cartesian coordinate system, starting from the first diffusion sampling point (x ij1 ,y ij1 ) to the final diffusion sampling point (x ij2 ,y ij2 vectors This can be represented by coordinate difference, i.e. This vector contains the direction from the first diffusion sampling point to the last diffusion sampling point and the distance between these two points. To obtain the unit vector representing the direction... The vector needs to be Divide by its modulus According to the formula for calculating the magnitude of a vector, in a two-dimensional plane, a vector... model vector Dividing each component (x-coordinate and y-coordinate) by its modulus yields the unit vector. The expression.

[0065] It should be noted that, in one specific embodiment, assuming that in the second sub-region of the first time period, the coordinates of the first diffusion sampling point are (1,2) and the coordinates of the last diffusion sampling point are (4,6), then the vector Its model Then the unit vector This unit vector represents the direction of diffusion of volatile organic compounds in this sub-region during this time period.

[0066] Wind direction and speed were acquired concurrently with the monitoring of volatile organic compounds (VOCs). These wind directions and speeds were then mapped one-to-one with each sampling point at each time point. The average values ​​were calculated to obtain the wind direction and speed for each sub-region at each time period. The influence coefficient of meteorological conditions on VOC diffusion at each time period was then calculated. Among them I ij 、W ij These represent the diffusion intensity and wind speed of the volatile organic compound concentration in the j-th sub-region during the i-th time period, respectively. These are the average values ​​of the diffusion intensity and wind speed of the volatile organic compound concentration during the i-th time period, respectively. φ1 and φ2 represent the unit direction vector of the wind direction in the j-th sub-region during the i-th time period, respectively, and represent the weighting factors of wind direction and wind speed. By corresponding meteorological data and calculating the influence coefficient, the influence of meteorological conditions (wind direction and wind speed) on the diffusion intensity of volatile organic compounds can be clarified, which helps to comprehensively assess the influencing factors of diffusion.

[0067] It should be noted that, in one specific embodiment, φ1 can be set to 0.5 and φ2 can be set to 0.5. The wind direction directly determines the diffusion path of volatile organic compounds (VOCs), guiding them from the pollution source to a specific direction. Different wind directions may cause VOCs to mix with the air in different areas, thus affecting their concentration distribution. The greater the wind speed, the stronger the dilution capacity of VOCs. In the atmosphere, strong winds can quickly diffuse VOCs over a larger spatial range, reducing their concentration in local areas. Therefore, the weights of wind direction and wind speed are equal.

[0068] It should be noted that the formulas for the influence coefficients of meteorological conditions on the diffusion of volatile organic compounds at different time periods are divided into: The study consists of two parts, corresponding to the effects of wind direction and wind speed on the diffusion of volatile organic compounds, and then combined using weights φ1 and φ2. The first part... Derived from the definition of vector dot product, Where θ is the angle between the two vectors, for a unit vector Its value reflects the angle between the diffusion direction of volatile organic compounds and the wind direction, thus demonstrating the influence of wind direction on diffusion. When the two directions are the same, This indicates that the wind direction is entirely favorable for diffusion; when the direction is opposite, This indicates that the wind direction completely suppresses diffusion; when perpendicular, This indicates that wind direction has no direct effect on diffusion, For all sub-regions The average is calculated to obtain the overall influence of wind direction on the diffusion direction of volatile organic compounds across the entire region, eliminating the local effects caused by sub-regional differences and reflecting the overall trend. (Part Two) It is to calculate the diffusion intensity I of volatile organic compounds. ij With wind speed W ij The covariance is used to measure the overall error between two variables. If the two variables have the same trend, that is, when one variable increases, the other also increases, or when one decreases, the other also decreases, the covariance is positive, indicating that wind speed and the intensity of volatile organic compound diffusion are positively correlated, and wind speed promotes diffusion. If the trends are opposite, the covariance is negative, indicating that wind speed inhibits diffusion. If the two changes have no obvious correlation, the covariance is close to zero. The correlation coefficient is obtained by dividing the covariance by the square root of the product of the two fluctuations. Standardizing the covariance so that its value is between [-1, 1] makes it easier to compare and analyze the tightness of the linear relationship between wind speed and the intensity of volatile organic compound diffusion.

[0069] The model building module is used to build models based on the diffusion direction and intensity of volatile organic compounds (VOCs) in each sub-region at each time period, as well as the influence coefficient of meteorological conditions on VOC diffusion at each time period, and output VOC diffusion predictions.

[0070] The specific operation method of the model construction module is as follows: The model is trained based on the diffusion direction and intensity of volatile organic compounds (VOCs) in each sub-region during each time period, as well as the influence coefficient of meteorological conditions on VOC diffusion during each time period. This generates a VOC diffusion prediction model. The location coordinates of the detection points for demand analysis, the demand analysis period, and their corresponding meteorological conditions are input into the trained model, and the VOC diffusion prediction is output. The model, trained based on multi-faceted data, can integrate key factors related to VOC diffusion. After inputting specific data, it outputs diffusion predictions, allowing for advance estimation of VOC diffusion and providing a basis for environmental management and pollution control.

[0071] It should be noted that the specific operational method for generating the volatile organic compound (VOC) diffusion prediction model is as follows: For the diffusion intensity of VOCs in each sub-region at each time period and the influence coefficient of meteorological conditions on VOC diffusion at each time period, the minimum and maximum values ​​are selected respectively, and then calculated using the formula... Standardize it, I' ij R' represents the standardized value of the diffusion intensity of volatile organic compounds in the j-th sub-region during the i-th time period. i I represents the standardized value of the influence coefficient of meteorological conditions on the diffusion of volatile organic compounds in the i-th time period. ij R represents the diffusion intensity of volatile organic compounds in the j-th sub-region during the i-th time period. i I represents the influence coefficient of meteorological conditions on the diffusion of volatile organic compounds during the i-th time period. max I min R represents the maximum and minimum values ​​of the diffusion intensity of volatile organic compounds, respectively. max R min These represent the maximum and minimum values ​​of the influence coefficient of meteorological conditions on the diffusion of volatile organic compounds (VOCs). For the diffusion direction of VOCs in each sub-region at each time period, a reference direction is set, and the angle of the diffusion direction of VOCs in each sub-region at each time period relative to the reference direction is obtained and converted into polar coordinates (r, θ). ij ), r takes a fixed value of 1, representing a unit vector, θ ij The polar angle representing the change in diffusion direction of volatile organic compounds in the j-th sub-region during the i-th time period is expressed by the formula. Standardize it, θ' ij θ represents the polar angle normalized value representing the diffusion direction of volatile organic compounds in the j-th sub-region during the i-th time period. max θ minThese represent the maximum and minimum polar angles after the diffusion direction of volatile organic compounds (VOCs) is transformed. A standardized dataset is obtained from this. The standardized dataset is divided into N parts, with N-1 parts used as the training set and 1 part as the validation set each time. These parts are then input into the model for training. This process is repeated N times to obtain the average performance index of the N models. Based on the average performance index, the optimal hyperparameter settings are determined. Using the determined optimal hyperparameters, the model is finally trained on all datasets to obtain the final VOC diffusion prediction model for practical applications.

[0072] The management database stores the collected parameters and volatile organic compound concentration data tables for each ambient air quality range.

[0073] This system determines the diffusion direction and intensity of volatile organic compounds (VOCs) by drawing isopleth maps of VOC concentrations. By collecting meteorological data from the same period, it analyzes the influence coefficient of meteorological conditions on VOC diffusion. By constructing models, it outputs VOC diffusion predictions, which helps to improve the intelligence and precision of VOC monitoring, enabling real-time and accurate understanding of the dynamic changes of VOCs and timely detection of potential pollution problems.

[0074] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention, which are still covered within the protection scope of the present invention.

Claims

1. A volatile organic compound (VOC) monitoring system for hydrocarbon removal, characterized in that, The system specifically includes the following modules: The sample collection module is used to collect air samples from the environment and perform preprocessing. The concentration distribution map generation module uses a spatial interpolation algorithm to draw isopleth maps of volatile organic compound concentrations based on the estimated concentration values ​​of interpolation points in each sub-region. The diffusion trend analysis module is used to determine the diffusion direction and intensity of volatile organic compounds by comparing the concentration changes at sampling points at adjacent time points; The specific operation steps of the diffusion trend analysis module include: S1. Select several sampling points from the volatile organic compound concentration contour maps of each sub-region at each time point according to a set interval. At the same time, take adjacent time points as a time period. Based on the volatile organic compound concentration contour maps of each sub-region at each time point, compare and obtain the change in volatile organic compound concentration of each sampling point in each sub-region in each time period. S2. Based on the changes in volatile organic compound concentrations at each sampling point in each sub-region at each time period, determine the diffusion direction of volatile organic compounds in each sub-region at each time period; S3. Calculate the diffusion intensity of volatile organic compounds in each sub-region and each sub-region at each time period based on the changes in volatile organic compound concentration at each sampling point in each sub-region. The meteorological impact analysis module is used to collect meteorological data for the same period and analyze the impact coefficient of meteorological conditions on the diffusion of volatile organic compounds. The model building module is used to build a model based on the diffusion direction and intensity of volatile organic compounds (VOCs) in each sub-region at each time period, as well as the influence coefficient of meteorological conditions on VOC diffusion at each time period, and output the diffusion prediction of VOCs. The management database is used to store the collected parameters and volatile organic compound concentration data tables corresponding to each ambient air quality range; The specific analysis method of the meteorological impact analysis module is as follows: Obtain the location coordinates of the first and last diffusion sampling points in the diffusion direction of volatile organic compounds in each sub-region at each time period. , Indicates the first The first time period The x and y coordinates of the first diffusion sampling point in each sub-region Indicates the first The first time period The x and y coordinates of the final diffusion sampling points in each sub-region are used to determine the diffusion direction of volatile organic compound (VOC) concentrations. This VOC diffusion direction is then converted into a vector and normalized to obtain the unit diffusion direction vector of VOCs in each sub-region at each time period. ; Wind direction and speed were acquired concurrently with the monitoring of volatile organic compounds (VOCs). These wind directions and speeds were then mapped one-to-one with each sampling point at each time point. The average values ​​were calculated to obtain the wind direction and speed for each sub-region at each time period. The influence coefficient of meteorological conditions on VOC diffusion at each time period was then calculated. ,in The first The first time period The diffusion intensity and wind speed of volatile organic compound concentration in each sub-region They are the first The average diffusion intensity and wind speed of volatile organic compound concentration over a given period. Indicates the first The first time period The unit direction vector of wind direction in each sub-region These represent the weighting factors for wind direction and wind speed, respectively. The specific operation method of the model construction module is as follows: Based on the diffusion direction and intensity of volatile organic compounds (VOCs) in each sub-region at each time period, as well as the influence coefficient of meteorological conditions on VOC diffusion at each time period, a VOC diffusion prediction model is generated. The location coordinates of the detection points for demand analysis, the demand analysis period and its corresponding meteorological conditions are input into the trained model, and the VOC diffusion prediction is output.

2. The volatile organic compound monitoring system for hydrocarbon removal according to claim 1, characterized in that, The specific operation method of the sample acquisition module is as follows: The environmental area to be sampled is divided into grids to obtain sub-regions. Detection points are set in each sub-region according to the principle of equal spacing. The single sampling duration of the hydrocarbon removal instrument is set, and time points are set according to the preset equal time interval principle. The sampling flow rate is set. Based on the single sampling duration of the hydrocarbon removal instrument, the ambient air at each detection point in each sub-region is collected by the hydrocarbon removal instrument at each time point to obtain air samples at each detection point in each sub-region at each time point. The collected air samples at each detection point in each sub-region at each time point are put into the hydrocarbon removal instrument, and the hydrocarbon substances are removed by adsorption technology.

3. The volatile organic compound monitoring system for hydrocarbon removal according to claim 1, characterized in that, Before collecting air samples, the sample collection module detects the air quality of the environmental area to be sampled, and provides real-time feedback based on the collection parameters corresponding to each environmental air quality range stored in the management database, dynamically adjusting the quantity and collection flow rate at each time point.

4. The volatile organic compound monitoring system for hydrocarbon removal according to claim 1, characterized in that, The specific analysis method for the concentration estimates of the interpolation points in each sub-region is as follows: The corresponding volatile organic compound (VOC) concentration values ​​are obtained from the air samples at each detection point and time point after processing. These values ​​are then organized according to the location coordinates of each detection point and the corresponding time point to form a VOC concentration data table, which is stored in the management database. Using each sub-region as an interpolation unit, the center point of each interpolation unit is selected as the interpolation point. The distance between each sub-region interpolation point and each detection point in its interpolation unit is obtained. The interpolation points in each sub-region and each detection point in its interpolation unit are numbered from closest to furthest distance, and the numbers are as follows: The concentration estimates of the interpolation points in each sub-region were calculated. ,in Indicates the first The sub-region numbering, , Indicates the first The interpolation points in each sub-region and their corresponding interpolation units. Distance between detection points Indicates the first The first interpolation point in the sub-region interpolation unit The concentration of volatile organic compounds corresponding to each detection point.

5. The volatile organic compound monitoring system for a hydrocarbon removal instrument according to claim 1, characterized in that, The specific operation method of the concentration distribution map generation module is as follows: The minimum and maximum values ​​are obtained from the concentration estimates of the interpolation points in each sub-region, and the concentration range of volatile organic compounds (VOCs) in the environmental area to be collected is obtained in turn. The interval of the contour lines is set, and the number of contour lines to be drawn is determined in combination with the concentration range of VOCs in the environmental area to be collected. At the same time, a color gradient is set for the concentration range of VOCs in the environmental area to be collected. The concentration estimates of the interpolation points in each sub-region are mapped onto the set color gradient, and the VOC concentration contour maps of each sub-region at each time point are drawn according to the color corresponding to the concentration estimates of the interpolation points in each sub-region.

6. The volatile organic compound monitoring system for a hydrocarbon removal instrument according to claim 1, characterized in that, The specific operation method of step S2 is as follows: S21. Sort the volatile organic compound (VOC) concentration contour maps of each sub-region at each time point according to the time point order. Read the location coordinates of each sampling point and the corresponding VOC concentration value at each time point in the VOC concentration contour maps of each sub-region at each time point. Record these as the VOC concentration values ​​of each sampling point in each sub-region at each time point. At the same time, take adjacent time points as a time period and calculate the difference between the VOC concentration values ​​of each sampling point in each sub-region at adjacent time points to obtain the change in VOC concentration of each sampling point in each sub-region at each time period. S22. If the change in volatile organic compound (VOC) concentration at a sampling point in a sub-region during a certain period is equal to 0, it means that the VOC concentration at that sampling point in that sub-region during that period remains unchanged. If the change in VOC concentration at a sampling point in a sub-region during a certain period is greater than 0, it means that the VOC concentration at that sampling point in that sub-region during that period increases. Conversely, if the change in VOC concentration at that sampling point in that sub-region during that period decreases, it means that the VOC concentration at that sampling point in that sub-region decreases. S23. Select the sampling points where the concentration of volatile organic compounds (VOCs) increases in each sub-region during each time period and record them as reference sampling points. Set the corresponding angle thresholds for each direction to divide the direction of each reference sampling point relative to the center point of each sub-region. If the concentration of VOCs at each reference sampling point in a certain direction in a certain sub-region increases, while the concentration of VOCs at each reference sampling point in other directions remains unchanged or decreases, then determine that direction as the diffusion direction of VOCs concentration in that sub-region during that time period. Thus, the diffusion direction of VOCs in each sub-region during each time period is obtained.

7. The volatile organic compound monitoring system for a hydrocarbon removal instrument according to claim 1, characterized in that, The specific operation method of step S3 is as follows: The duration of each time period is obtained, and reference sampling points along the diffusion direction of volatile organic compounds in each sub-region of each time period are selected and denoted as diffusion sampling points. Adjacent diffusion sampling points are grouped together, and the straight-line distance between adjacent diffusion sampling points in each group of each sub-region is obtained. The concentration changes of volatile organic compounds at adjacent diffusion sampling points in each time period, sub-region, and group. , Indicates the first The number of each time period, , Indicates the first The numbering of adjacent diffusion sampling points in a group, Through formula The diffusion intensity of volatile organic compounds in each sub-region at each time period was obtained. ,in Indicates the first The number of groups of adjacent diffusion sampling points within each sub-region Indicates the first The duration of each time period.

Images